Prediction of Temperature after Steel Chemical Heat by Means of Neural Networks and Regression

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27360%2F09%3A00022102" target="_blank" >RIV/61989100:27360/09:00022102 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Prediction of Temperature after Steel Chemical Heat by Means of Neural Networks and Regression

Popis výsledku v původním jazyce

Contribution deals with application of artificial neural networks Metallurgical processes belong to complex physical-chemical processes theoretically described by means of multidimensional generally nonlinear dynamic systems with different transfer lagsin their structure. Before realization of these systems control requested by practice it is necessary to execute their structural and parametric identification. As these processes are very complex, all exact relations for their mathematical description are not known so far. There is certain chance to determine a proper system internal structure at system identification by means of statistical analysis, though this approach is knowledge and time-consuming. Identification by means of neural networks enables rather external system description, when we get an acceptable accordance between real and modelled outputs. This approach is thus more suitable for control than for identification itself. Contribution deals with a possibility of predic

Název v anglickém jazyce

Prediction of Temperature after Steel Chemical Heat by Means of Neural Networks and Regression

Popis výsledku anglicky

Contribution deals with application of artificial neural networks Metallurgical processes belong to complex physical-chemical processes theoretically described by means of multidimensional generally nonlinear dynamic systems with different transfer lagsin their structure. Before realization of these systems control requested by practice it is necessary to execute their structural and parametric identification. As these processes are very complex, all exact relations for their mathematical description are not known so far. There is certain chance to determine a proper system internal structure at system identification by means of statistical analysis, though this approach is knowledge and time-consuming. Identification by means of neural networks enables rather external system description, when we get an acceptable accordance between real and modelled outputs. This approach is thus more suitable for control than for identification itself. Contribution deals with a possibility of predic

Druh

D - Stať ve sborníku

CEP obor

JG - Hutnictví, kovové materiály

OECD FORD obor

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Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2009

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

10th International Carpathian Control Conference

ISBN

8389772-51-5

ISSN

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e-ISSN

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Počet stran výsledku

4

Strana od-do

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Název nakladatele

Faculty of Mechanical Engineeing and Robotics, AGH - University of Sciece and Technology

Místo vydání

Zakopane

Místo konání akce

Zakopane

Datum konání akce

27. 5. 2009

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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